Leaf disease detection on cucumber leaves using multiclass Support Vector Machine

2017 ◽  
Author(s):  
P. Krithika ◽  
S. Veni
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Disease Detection ◽  
Leaf Disease ◽  
Multiclass Support Vector Machine

A Novel Disease Detection and Classification Method Using Improved Fusion Random Weight Support Vector Machine

2021 ◽  
Vol 11 (12) ◽  
pp. 2976-2986
Author(s):  
M. Usha Rani ◽  
N. Saravana Selvam
Keyword(s):  
Support Vector Machine ◽  
Human Health ◽  
Health Informatics ◽  
Research Work ◽  
Noise Removal ◽  
Support Vector ◽  
Computational Techniques ◽  
Disease Detection ◽  
Successful Implementation ◽  
Leaf Disease

Health informatics is one of the main branch of engineering which provides a solution to a variety of problems like delayed, missed or incorrect diagnoses with the help of computational techniques. With the help of technologies such as bio-computing, health informatics, the disaster impacts on both human health and biological factors can be reduced to a large extend. Using these computational technologies, the country’s economy can also get boosted up and due to increased disease-causing pathogens, which directly impact the human health system. In this research work, a different type of sugarcane disease is detected and classified because manual identification is difficult and time-consuming. So, the farmers couldn’t find a better solution, than on the whole, they go for stubble burning, which is an alarming issue both on human and environmental wellness. The burning of bagasse causes bagassois, an interstitial lung disease that affects the tissues present in the lung through the air sacs. So, this sugarcane disease detection needs to be done early to avoid various health and environmental issues. The proposed work consists of the detection of four types of sugarcane leaf disease directly from the field. The sequence of methods is capturing images with WSN nodes, pre-processing with image enhancement and noise removal (IENR), segmentation with Fuzzy membership function and clustering (FMFC), feature extraction using Gray Level Co-occurrence Matrix Vector (GLCMV) and classification using Support Vector Machine (SVM). With the help of the effective proposed method, the highest parameters like precision, accuracy, sensitivity, and specificity for sugarcane leaf disease have been obtained. Based on the successful implementation process, the accuracy stated for the four sugarcane diseases along with the execution time is given below as Smut disease (87.12, 1.01 sec), Rust disease (90.23, 1.02 sec), Grassy Shoot disease (95.34, 1.047 sec), Red Rot disease (95.51, 1.04 sec).

Leaf Disease Detection using Support Vector Machine

2020 ◽  
Author(s):  
Debasish Das ◽  
Mahinderpal Singh ◽  
Sarthak Swaroop Mohanty ◽  
S. Chakravarty
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Disease Detection ◽  
Leaf Disease

Agarwood Oil Quality Grading using OVO Multiclass Support Vector Machine

2021 ◽  
Author(s):  
A. F. M. Amidon ◽  
N. Z. Mahabob ◽  
M. H. Haron ◽  
N. Ismail ◽  
Z. M. Yusoff ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Oil Quality ◽  
Support Vector ◽  
Quality Grading ◽  
Multiclass Support Vector Machine ◽  
Agarwood Oil
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